The basis function approach for modeling autocorrelation in ecological data

Trevor J. Hefley, Kristin M. Broms, Brian M. Brost, Frances E. Buderman, Shannon L. Kay, Henry R. Scharf, John R. Tipton, Perry J. Williams, Mevin B. Hooten

Research output: Contribution to journalArticle

34 Scopus citations

Abstract

Analyzing ecological data often requires modeling the autocorrelation created by spatial and temporal processes. Many seemingly disparate statistical methods used to account for autocorrelation can be expressed as regression models that include basis functions. Basis functions also enable ecologists to modify a wide range of existing ecological models in order to account for autocorrelation, which can improve inference and predictive accuracy. Furthermore, understanding the properties of basis functions is essential for evaluating the fit of spatial or time-series models, detecting a hidden form of collinearity, and analyzing large data sets. We present important concepts and properties related to basis functions and illustrate several tools and techniques ecologists can use when modeling autocorrelation in ecological data.

Original languageEnglish (US)
Pages (from-to)632-646
Number of pages15
JournalEcology
Volume98
Issue number3
DOIs
StatePublished - Mar 1 2017

All Science Journal Classification (ASJC) codes

  • Ecology, Evolution, Behavior and Systematics

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    Hefley, T. J., Broms, K. M., Brost, B. M., Buderman, F. E., Kay, S. L., Scharf, H. R., Tipton, J. R., Williams, P. J., & Hooten, M. B. (2017). The basis function approach for modeling autocorrelation in ecological data. Ecology, 98(3), 632-646. https://doi.org/10.1002/ecy.1674